Analyzing microtextured regions of optically anisotropic materials

ABSTRACT

Images of samples that are illuminated with polarized light are captured. Azimuth and inclination data are extracted from the captured images. The azimuth and inclination data are used to quantify MTRs.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/431,161, filed on Jun. 4, 2019, now allowed, which claims the benefitof U.S. Provisional Patent Application Ser. No. 62/680,093, filed Jun.4, 2018, entitled “ANALYZING MICROTEXTURED REGIONS OF OPTICALLYANISOTROPIC MATERIALS”, the disclosure of which is hereby incorporatedby reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under FA8650-17-P-5039awarded by Air Force Research Laboratory. The government has certainrights in the invention.

BACKGROUND

The present disclosure relates generally to analyzing materials and,more particularly, to systems and methods for analyzing microtexturedregions.

Materials science deals with the fundamental properties andcharacteristics of materials. For instance, the field of materialsscience often attempts to explore the relationship between the structureof materials at atomic or molecular scales (i.e., microstructure), aswell as the macroscopic properties of such materials. By studying howdifferent processes affect materials, and by studying how such materialsperform under different conditions, an understanding of the limitationsand capabilities of materials can be identified and predicted. Thus,there are ongoing efforts to characterize materials in various ways.

SUMMARY

The present disclosure provides systems and methods for analyzingmicrotextured regions (MTRs) in samples of optically anisotropicmaterials. Briefly described, in architecture, the disclosed embodimentscapture images of samples that are illuminated with various polarizationstates of light and extract orientation data of the optic axis from thecaptured images. The optic axis orientation data are used to quantifyMTRs.

According to aspects of the present disclosure, a system for performingmicrotexture analysis comprises a light source for emitting light.Further, a polarization state generator (e.g., linear polarizer) havinga first polarization axis receives the emitted light from the lightsource and produces a polarized light from the received light. A beamsplitter receives the polarized light and redirects the received lightto an objective lens that focuses the redirected light onto a samplethat has an optical anisotropy. Moreover, the objective lens receives areflected light (which polarization state is changed as a function ofthe optical anisotropy) from the sample and propagates the reflectedlight back to the beam splitter. Further, the system includes apolarization state analyzer that has a second polarization axis that isorthogonal or nearly orthogonal to the first polarization axis, and theanalyzer receives the propagated reflected light from the beam splitterand produces a partially extinguished light, which is a function of thefirst polarization axis, the second polarization axis, and the opticalanisotropy of the sample. Further, the system includes a rotatablesample positioning device capable of producing rotation of the samplerelative to the polarization state generator and polarization stateanalyzer. Moreover, the system includes a camera to capture an image ofthe partially extinguished light at several rotation angles and aprocessing system to quantify data from the captured set of images byflat-field correcting the captured images, performing image rotation andimage registration, extracting an amplitude and a phase value for thecaptured image intensity variation at each corresponding pixel of thecaptured image set, correlating the amplitude with an inclination of theoptic axis, correlating the phase with an azimuth of the optic axis, andquantifying microtextured regions (MTRs) using the inclination andazimuth by relating the optic axis to crystallographic orientation.

Other systems, devices, methods, features, and advantages will be orbecome apparent to one with skill in the art upon examination of thefollowing drawings and detailed description. It is intended that allsuch additional systems, methods, features, and advantages be includedwithin this description, be within the scope of the present disclosure,and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the disclosure can be better understood with referenceto the following drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a flow diagram showing one embodiment of a system foranalyzing microtextured regions (MTRs) of a sample, according to variousaspects of the present disclosure;

FIG. 2 is a block diagram of embodiments of an optical orientationmeasurement device for use in the system for analyzing MTRs of a sample,according to various aspects of the present disclosure;

FIG. 3 is a block diagram of an alternate embodiment of an opticalorientation measurement device for use in the System for analyzing MTRsof a sample, according to various aspects of the present disclosure;FIG. 4 is a flowchart showing one embodiment of a process for collectingMTR data, according to various aspects of the present disclosure;

FIG. 5 is a flowchart showing one embodiment of a process forquantifying the collected MTR data, according to various aspects of thepresent disclosure; and

FIG. 6 is a diagram of an exemplary computer processing system forimplementing the methods and processes described more fully herein.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Various characteristics of materials affect their strength andsusceptibility to failure. For example, microtextured regions (MTRs) intitanium (Ti) contribute to catastrophic failures under cold dwellfatigue loading conditions. These MTRs in Ti comprise clustered alphagrains (a-Ti) with a certain crystallographic orientation that resultsfrom thermomechanical processing of Ti alloys. Because MTRs in Ticompromise a structural integrity of the materials, it is important toproperly quantify the MTRs.

Conventional approaches to quantifying MTRs include electron backscatterdiffraction (EBSD) and spatially resolved acoustic spectroscopy (SRAS).Although EBSD accurately quantifies crystallographic orientation as aninput for identifying MTRs, EBSD scans are both expensive andtime-consuming. Thus, EBSD scanning for MTRs becomes bothcost-prohibitive and time-inefficient for quantifying large areas and/ornumbers of samples. Although SRAS is less expensive than EBSD forquantifying MTRs, SRAS suffers from a lower resolution and limitedorientation information, which results in less accuracy than EBSD.

To ameliorate the problems related to cost, speed, and accuracy, thepresent disclosure provides systems and methods for analyzing MTRs usingpolarimetric imaging to extract azimuth and inclination data of theoptic axis at various locations in a sample. Making use of thecorrelation between optic axis and basal pole (i.e. (0001) pole) inmetallic materials having hexagonal crystal symmetry, this data can beassociated with MTRs and, thus, provide an ability to quantify the MTRsaccurately, quickly, and cost-effectively. Broadly, the presentdisclosure teaches capturing of images of samples that are illuminatedwith monochromatic polarized light. Both azimuth and inclination data ofthe optic axis (which can be converted to Euler angles for futureanalysis) are extracted from the captured images. The azimuth andinclination data are used to quantify MTRs.

Having provided a broad technical solution to a technical problem,reference is now made in detail to the description of the embodiments asillustrated in the drawings. Specifically, FIG. 1 shows one embodimentof a block diagram of a system for quantifying MTRs, while FIGS. 2 and 3show embodiments of processes for quantifying MTRs. Although severalembodiments are described in connection with these drawings, there is nointent to limit the disclosure to the embodiment or embodimentsdisclosed herein. On the contrary, the intent is to cover allalternatives, modifications, and equivalents. In addition to FIGS. 1through 3 , specific embodiments of different components and processsteps are also described in greater detail, below.

Overall Flow Diagram

Referring now to the drawings, and particularly to FIG. 1 , a flowdiagram 100 illustrates a process for analyzing (e.g., identifying,quantifying, etc.) microtextured regions in materials with an orderedcrystal structure, according to aspects of the present disclosure.

The process obtains crystallographic orientation information from asample of a material under evaluation at 102. The orientationinformation may be derived from an existing data set, or the orientationinformation can be extracted from the sample under evaluation using anorientation measurement device. Several embodiments of the orientationmeasurement device are discussed below.

The orientation information obtained from the material sample ischaracterized in either sub-regions (i.e., “tiles”) of a large area, ora large area is characterized and then split into tiles (i.e.,sub-regions of the large area). Regardless, the orientation informationis arranged into tiles that correspond to associated spatial locationsof the sample. The tiles may contain information of one or morearbitrary dimensions (i.e. n-dimensions where n is any integer greaterthan 0). However, at least one of those dimensions should contain dataabout a location specific crystallographic orientation state (e.g. Eulerangles, Rodrigues vector, Orientation matrix, etc.). The collection ofinformation about the orientation state at multiple locations is thenregarded as the texture.

Texture processing is performed at 104. In this regard, the tileprocessing discussed above may be implemented as a separate process(such as may be implemented before texture processing) or the above-tileprocessing may be implemented as part of the texture processing at 104.

As noted above, each measurement (in whatever form is provided by theparticular orientation measurement technology) is mapped to acorresponding orientation matrix. Thus, there is an orientation matrixfor each pixel of each tile. For every tile, the Generalized SphericalHarmonic (GSH) expansion is computed from the corresponding orientationmatrices, which transforms the measurements to an “orientation space”.For instance, in an illustrative implementation, data from each tile isprocessed such that “texture information” along with non-textureinformation (e.g., information about the quality and pedigree of thecorresponding data, etc.), form a hybrid descriptor that is able todiscern, for instance, the relevance of a given data point in relationto belonging to a calculated zone of microtexture.

The hybrid descriptors are inputted into a clustering device at 106. Theclustering device outputs dominant zones of microtexture (also referredto herein as orientation clusters). In an illustrative implementation, ak-means clustering algorithm is used to find the most likelyrepresentative hybrid orientation descriptor such that the inner-classmean to the class centroids are minimized. However, in general, anymethodology, algorithm, or combination of methodologies which provides amapping from orientation descriptor to class label could be used in theplace of the k-means clustering, which is discussed only as an example.

The orientation clusters are mapped to real space at 108. In anexemplary implementation, the dominant zones of microtexture output bythe clustering device at 106 are spatially resolved at 108, back on tothe original, corresponding spatial location in the sample. Theorientation clusters are mapped back to the tiles and back to theoriginal coordinate system from which they were collected.

Spatial filtering is performed at 110. Keeping with the above-exemplaryimplementation, local, spatial filtering is performed such thatcontinuous regions of similar zones of microtexture are identified.Zones of microtexture are characterized by a representative orientationdescriptor or collection of descriptors. In an illustrativeimplementation, data points that either do not belong to a coherent zoneof microtexture or are of low quality are then labeled as outliers. Inthis instance a local voting scheme can be used to decide which zone ofmicrotexture a given location belonged to in the case that there aremultiple adjacent zones of microtexture.

A macrozone identification process can be performed at 112 to identifymacrozones in the orientation descriptors. Moreover, a graphicalpresentation of orientation information of multiple datasets isimplemented by plotting a continuous color-coded map (i.e., c-map (i.e.,a c-axis orientation map)) relating the c-axis orientation (i.e. (0001)pole) to the sample coordinate system at 114. This graphicalpresentation comprises a pole figure map which is the opposite of theinverse pole figure (IPF) map known to those familiar with the art. Forinstance, c-maps can be generated for each individual dataset (i.e.,tile). The images of the c-maps can then be divided into one image ormultiple images.

Further, higher-order statistics can be processed at 116. In anillustrative example, macrozones identified at 112 are obtained andmetrics on the macrozones are computed, including n-point statistics(e.g. 2-point statistics). The n-point statistics are applied to analyzethe material under test. In this regard, the data can be expressed as acontinuous function, i.e., a vector having a size the same as the imagesize. Thus, an m×m pixel image has an m² dimension space (also referredto herein generally as n-point). By way of illustration, and not by wayof limitation, a 200×200 pixel image has 40,000 2-point statistics. Byexpanding out to a 40,000-dimension space, the image reduces to a singlepoint. By obtaining a plurality of different data sets, each data sethaving the same dimensions (40,000 in this example), and by plottingeach data set as a single point in space, trend analysis can beperformed.

The overall texture of the sample is then partitioned into theorientation descriptors that comprise each zone of microtexture at 118.This allows an investigator to compute or derive information on regionsof microtexture that they would normally perform on the level of thewhole sample.

According to still further aspects of the present disclosure, theobtained data, including the microstructure metrics, n-pointinformation, etc., are stored in a long-term data storage and archivalsystem for subsequent use at 120. The stored information can beretrieved for performing quality control at 122. For instance,microstructure metrics can be retrieved from storage for comparisonacross different data sets and samples. This allows, as an example, anengineer to make decisions about quality control of thermo-mechanicalprocessing.

As another example, the data stored in the database at 120 can beevaluated for various data mining operations at 124. More particularly,n-point information is stored, mapped, processed, searched and/orotherwise manipulated to perform analysis of orientation information.

The flow described above is described in greater detail in U.S. Pat. No.9,070,203 to Salem et al. issued on Jun. 30, 2015, the entirety of whichis hereby incorporated by reference.

Embodiments of Orientation Measurement Device

According to aspects of the present disclosure, embodiments of theorientation measurement device 200 is shown in FIG. 2 . As shown in FIG.2 , a light source 210 emits light that is received by a linearpolarizer 220, which has a polarization axis and produces linearlypolarized light from the received light. The linearly polarized light isreceived by a beam splitter 240, which redirects the linearly polarizedlight to an objective lens 250. The objective lens 250 focuses the lightonto a sample 260 that has an optical anisotropy. The sample 260reflects light that is affected by the sample's optical anisotropy. Thereflected light from the sample 260 is propagated to the beam splitter240 by the objective lens 250 and further from the beam splitter 240 tothe analyzer 270, which has a polarization axis that is generallyorthogonal (i.e., orthogonal or nearly orthogonal) to the polarizationaxis of the polarizer 220. The analyzer 270 receives the light from thebeam splitter 240 and produces a partially extinguished light.

The partially extinguished light is a function of the polarization axisof the linear polarizer 220, the polarization axis of the analyzer 270,and the optical anisotropy of the sample 260. In a system without asample (i.e., in the case of optically isotropic reflection), thepartially extinguished light would actually be fully extinguished light,because the polarization axis of the polarizer 220 is orthogonal to thepolarization axis of the analyzer 270. However, in a system with asample under test, the light reflected off the sample 260 will includedifferent characteristics than the light from the linear polarizer 220.Thus, the light through the analyzer will only be partially extinguishedinstead of fully extinguished. The partially extinguished light iscaptured by a camera 280.

To obtain enough information in the measurements to determineorientation of the sample, the sample should be rotated a total ofone-hundred-and-eighty degrees by smaller increments (i.e., rotationangle increment). By rotating the sample 260 and collecting multiplepartially extinguished images, both the azimuth and inclination data canbe determined. For example, a measurement may be taken of a sample, thenthe sample is rotated by a rotation angle increment and anothermeasurement is taken. By rotating the sample 260, the effect of thesample 260 on the polarized light from the linear polarizer 220 ischanged and a modulated intensity is captured by the camera 280 at eachspatial pixel location. By measuring the sample 260 at several differentrotation angles, the measurements of modulated intensity can beinterrogated to extract the phase and amplitude of the modulation, whichcan be used to indicate the azimuth and inclination data of the sample'soptic axis. The azimuth and inclination data are then used to quantifyMTRs in the sample 260. using a mapping between the azimuth andinclination data to orientation of basal (0001) poles to identify MTRsin the sample. As mentioned above, after a sample is quantified at onetile, the sample (or the orientation measurement device) may be moved tointerrogate another tile of the sample.

In another embodiment of the orientation measurement device describedherein, instead of rotating the sample 260, the linear polarizer 220 andthe analyzer 270 are rotated by the rotation angle increment such thatthe polarization axis of the polarizer 220 remains orthogonal to thepolarization axis of the analyzer 270 as measurement is taken. Thus, thesample remains in place, while the light is polarized along a differentaxis relative to the sample 260. This method may be desirable in someinstances, because when the sample is rotated, the sample may not bedirectly centered on the axis of rotation of the sample. Thus, when thesample is rotated, a correction algorithm needs to align the sample forthe different measurements. However, if the polarizer 220 and theanalyzer 270 are rotated instead of the sample, then such a correctionalgorithm is not required, though the modulation response measured bythe camera 280 will require additional calibration to account for theeffects of the beam splitter 240 and other optical elements.

In another embodiment of the orientation measurement device describedherein, the polarizer 220 is not a linear polarizer. Instead, thepolarizer 220 is a circular polarizer 220. For example, the circularpolarizer 220 may be a linear polarizer with a quarter-wave plate afterthe linear polarizer oriented appropriately with respect to the linearpolarizer to produce circular polarization. Thus, the analyzer 270 mayrotate to different angles at separate rotation angle increments to geta series of measurements of the sample at different rotation angles overtime. This embodiment reduces the number of moving parts to only theanalyzer and does not require the correction algorithm required whenrotating the sample.

Another embodiment of the orientation measurement device describedherein is shown in FIG. 3 . Similar to the embodiments described in FIG.2 , the embodiment the orientation measurement device 300 of FIG. 3comprises a monochromatic light source 310 and a fixed linear polarizer320. Moreover, after the linear polarizer 320, the embodiment includesan additional polarizing element or group of polarizing elements 330that include appropriately oriented liquid crystal variable retarders tocreate an arbitrary polarization state generator, so the light after thesecond set of polarizing elements 330 is controllably and arbitrarilyelliptically polarized. The elliptically polarized light is redirectedby a beam splitter 340 to an objective lens 350. The objective lens 350focuses the light on the sample 360, which then reflects the light. Thereflected light propagates back through the objective lens 350 to thebeam splitter 340 and continues through an analyzer 370 to a camera 380.In the embodiment of FIG. 3 , the analyzer 370 is a fixed circularpolarizer. Thus, the polarizer 320 is fixed, the analyzer 370 is fixed,and the sample 360 is fixed. While the sample is described as fixed, thesample (or the orientation measurement device) may be moved laterally inan X or Y direction to interrogate another tile of the sample, aspreviously described.

The embodiments of FIGS. 2-3 can have alternate embodiments. Forexample, instead of a fixed circular polarizer and a rotating linearanalyzer, the polarizer may be linear and rotatable, and the analyzermay be a fixed circular analyzer. As another example, instead of anelliptical polarizer and a circular analyzer, the polarizer may be acircular polarizer and the analyzer may be an elliptical analyzer. Foreach alternate embodiment, calibration will be needed to correlate themeasured intensity response to the orientation of the optic axis at eachsample location.

The camera 280, 380 captures an image formed by the entering beam. Todetermine azimuth and inclination data of the optic axis (i.e., thecrystallographic axis)), multiple images (i.e., an image sequence) arecaptured comprising various polarization response states through eitherthe sample 260, 360 and/or the polarizer 220, 330 and/or the analyzer270, 370 being rotated in a controlled fashion resulting in changes ofimage intensity captured by the camera 280, 380. This rotation can beaccomplished via physical rotation or through electronic modulation inthe case of liquid crystal devices, as discussed herein. The azimuth andinclination data obtained through analysis of the collected series ofimages (i.e., an image sequence) are then used to quantify MTRs in thesample 260, 360.

Having provided several embodiments of an orientation measurement device200, 300, attention is now turned to FIGS. 4 and 5 , which areflowcharts showing broad embodiments of processes for collecting MTRdata and analyzing MTR data. As shown in FIG. 4 , one embodiment of aprocess 400 comprises positioning a sample at 410. For reference, thesample is positioned at a designated rotational angle (or a referenceangle). The positioned sample is illuminated at 420 with a polarizedmonochromatic light, which results in a reflection of the light by thesample. An image of the sample is then captured at 430. At 440, theprocess 400 determines whether or not images from all rotational angles,polarization states, or both have been captured (i.e., whether an imagesequence (i.e., a series of images) is complete). If there are morerotational angles from which to capture images, then the process 400performs a rotation (e.g., rotates the sample, rotates the linearpolarizer and linear analyzer, rotates the linear analyzer used with acircular polarizer, or performs a discrete data capture in a system witha liquid crystal variable polarizer and a circular analyzer), at 450,and the process 400 continues by illuminating the sample at 420. As onecan appreciate, by performing the rotation at given angular intervals,the process 400 acquires information on a sample's anisotropy. When theprocess 400 determines that images have been captured from all relevantangles, then the process continues to FIG. 5 .

FIG. 5 is a flowchart showing one embodiment of a process 500 foranalyzing and quantifying the collected MTR data. For some embodiments,a computer system (not shown) is used to control the process 400 of FIG.4 and, also, to perform the quantification process 500 of FIG. 5 . Withthis in mind, and continuing from the process 400 of FIG. 4 , thecaptured images are flat-field corrected at 510. Thereafter, in someembodiments at 520, the process 500 performs image rotation and/or imageregistration to align the captured images. From each pixel of thealigned images, properties of intensity modulation at each pixel overthe series of images are extracted at 530. The extracted intensitymodulation is correlated with azimuth and inclination of the optic axis(i.e., crystallographic axis) at 540 according to a polarizationresponse model for the sample and optical system. From the azimuth andinclination, the process 500 quantifies the MTR at 550.

Generally, the orientation of the optic axis (i.e., amplitude and phasevalues) can be extracted by using Mueller matrices. For example,designating S^(OUT) as a Stokes vector at each pixel, whose intensitycomponent S₀ is captured at each pixel location by camera 280,380 andencodes the orientation information for the sample at each pixel,S^(OUT) can be written as:

S ^(OUT) =M ^(PSA) M ^(BS-TRANS) M ^(SAMPLE) M ^(BS-REFL) M ^(PSG) S^(IN)  [Eq. 1]

with S^(IN) representing an input Stokes vector from the monochromaticlight, M^(PSG) representing a Mueller matrix for a polarization stategenerator (PSG, e.g., polarizer), M^(BS-REFL) representing a Muellermatrix for the beam splitter when reflecting the input light towards thesample, M^(SAMPLE) representing an orientation-dependent Mueller matrixfor the M^(BS-TRANS) sample, representing a Mueller matrix forpolarization effects of a beam splitter under transmission afterreflection from the sample, and M^(PSA) representing a Mueller matrixfor a polarization state analyzer (PSA, e.g., analyzer). The matrixM^(SAMPLE) can be derived by inversion if the Mueller matrix of the PSGand PSA are well-characterized. Calibration of the beam splitter can beaccomplished by measuring a known sample whose Mueller matrix M^(MEAS)is known by other means. Upon deriving M^(MEAS), M^(SAMPLE) can bewritten as:

M ^(SAMPLE)=(M ^(BS-TRANS))⁻¹ M ^(MEAS)(M ^(BS-REFL))⁻¹  [Eq. 2].

The optic axis orientation of the sample at each image location can beextracted from the derived M^(SAMPLE) It may not be necessary to haveknowledge of every component of the sample Mueller matrix M^(SAMPLE) inorder to describe the orientation response of a sample observedaccording to the various embodiments described herein.

The orientation measurement device may be calibrated using a forwardmapping and/or forward model and a known sample. Basically, a samplewith a known structure is placed in the orientation measurement device(e.g., any of the embodiments described herein), and the light receivedby the camera after several rotations is mapped to the known orientationof the sample at a particular pixel location on the sample for a givenmeasurement tile. Thus, a mapping from the light received (i.e.,modulation over several images (i.e., image sequence)) to an orientationis created from the calibration. This mapping may be any desired type ofmapping. For example, equations may be created such that when themeasurements are taken, the equations will produce the orientation ofthe c-axis (i.e., optic axis) at that tile in the sample. As anotherexample, the calibration can create values for a look-up table (LUT), sothe amplitude and/or phase values may be used as inputs to the LUT toget the orientation values for the c-axis. The LUT version takes morestorage space but will produce orientation results quicker than theequation version. In some embodiments, a fast Fourier transform (FFT)may be used to evaluate the amplitude and/or phase of the response usinga minimum number of images in the image sequence sufficient to samplethe finite bandwidth of the response.

In one preferred embodiment and referring back to FIGS. 2-3 , the camera280, 380 is a charged coupled device (CCD) camera. However, as one canappreciate, other image-acquisition devices can be used to capture thelight that is reflected from the sample 260, 360.

If imaging of a larger sample is either needed or desired, then someembodiments provide a moving platform (not shown) for the sample 260,360, thereby permitting the sample 260, 360 to move laterally withreference to the camera 280, 380. Alternatively, to allow differentrelative positions of the sample 260, 360 with reference to the camera280, 380, one embodiment comprises a gantry system that permitscoordinated movement of the light source 210, 310, the linear polarizer220, 320, the beam splitter 240, 340, the objective lens 250, 350, theanalyzer 270, 370, and the camera 280, 380 with reference to the sample260, 360. In either case, the system may comprise a controller and astepper motor or servo motor for controlling the relative positions,rotational angles, or both between the sample 260, 360 and the imagingcomponents 210, 310, 220, 320, 250, 350, 270, 370, 280, 380, etc. Also,those having skill in the art will appreciate that the relative rotationof the sample can range from zero degrees (0°) to 360° (or, in somecases, 180°) in substantially equal angular increments, such as, forexample, 5°, 10°, 15°, 20°, 30°, 45°, etc.

As mentioned above, several different configurations may be used toprovide the rotations required for the data collection. For example, thepolarizer and the analyzer may be rotated synchronously with each other(and possibly along with the beam splitter and the objective lens) usinga stepper motor or a servo motor and a controller. As another example,circular polarization through the linear polarizer and quarter-waveretarder may be used with a rotating analyzer. A further exampleincludes a rotating polarizer and a circular analyzer. A fourth exampleuses a pair of liquid crystal variable retarders to completely eliminatea need for moving parts in the optical train. Liquid crystal retarderswork by varying the orientation of anisotropic nematic phase liquidcrystals using a variable electrical field. By operating the firstliquid crystal variable retarder, for example, at a nominal retardanceof one-fourth wavelength, operating the second at a nominal retardanceof one-half wavelength, and applying a sequential perturbation to each,a polarization state can be tailored to characterize the sampleretardance with a minimum number of images in the image sequence.

Next, for some embodiments, the objective lens 150 focuses light onto asurface of the sample 260, 360 (also designated as criticalillumination), while for other embodiments the image of the light sourceis focused on a back focal plane of the objective lens 260, 360 (alsodesignated as Kohler illumination).

Additionally, although a coaxial system is shown with reference to FIG.2 , it should be appreciated that, in another embodiment, the systemcomprises a dual-axis illumination system (not shown). Rather thanhaving a single objective lens 250, 350 with a beam splitter 240, 340(as shown in FIGS. 2-3 ), a dual-axis illumination system eliminates thebeam splitter and employs two objective lenses in the optical pathway.

Also, it should be appreciated that the processes of FIGS. 4 and 5(e.g., positioning, illuminating, capturing, flat-field correcting,performing image rotation and image registration, extracting modulation(e.g., amplitude and phase values), correlating azimuth and inclinationto the phase and amplitude, and quantifying the MTRs) may be implementedin hardware, software, firmware, or a combination thereof. In thepreferred embodiment(s), the computer process steps are implemented insoftware or firmware that is stored in a memory and that is executed bya suitable instruction execution system. If implemented in hardware, asin an alternative embodiment, the computer process steps can beimplemented with any or a combination of the following technologies,which are all well known in the art: a discrete logic circuit(s) havinglogic gates for implementing logic functions upon data signals, anapplication specific integrated circuit (ASIC) having appropriatecombinational logic gates, a programmable gate array(s) (PGA), a fieldprogrammable gate array (FPGA), etc.

Using the systems and processes described herein, MTR data may becaptured over a large area (e.g., 100 millimeters (mm) by 100 mm area)while maintaining a high spatial resolution (e.g., approx. 1-2 microns(μm)) through a combination of translations of the sample and/or opticalsystem. The size of the scanned area is limited only by the extent ofthe translation apparatus.

As discussed above, the extraction of MTR data (e.g., crystallographicorientation) is based on amplitude (brightness) change of a series ofimages (i.e., image sequence) at each corresponding pixel. Thus, thesystem should be calibrated to compensate for light source brightness,sample reflectance, camera response, etc.

Referring to FIG. 6 , a schematic of an exemplary computer system havingcomputer readable program code for executing aspects described hereinwith regard to the preceding FIGURES. The computer system 400 includesone or more microprocessors 410 that are connected to memory 420 via asystem bus 430. A bridge 440 connects the system bus 430 to an I/O Bus450 that links peripheral devices to the microprocessor(s) 410.Peripherals may include storage 460, such as a hard drive, removablemedia storage 470, e.g., floppy, flash, CD and/or DVD drive, I/Odevice(s) 480 such as a keyboard, mouse, etc. and a network adapter 490.

The memory 420, storage 460, removable media insertable into theremovable media storage 470 or combinations thereof, can be used toimplement the methods, configurations, interfaces and other aspects setout and described herein. Thus, the computer system 400 may be used toimplement a machine-executable method for capturing features ofinterests in materials, according to one or more of the methods set outherein. In this regard, the memory 420, storage 460, removable mediainsertable into the removable media storage 470 or combinations thereof,can implement computer-readable hardware that stores machine-executableprogram code for capturing features of interests in materials, whereinthe program instructs a processor (e.g., microprocessor 410) to performone or more of the methods set out herein.

Still further, the exemplary computer system may be implemented as anapparatus for capturing features of interests in materials, which maycomprise a processor (e.g., microprocessor 410) coupled to a memory(e.g., memory 420, storage 460, removable media insertable into theremovable media storage 470 or combinations thereof), wherein theprocessor is programmed for capturing features of interests in materialsby executing program code to perform one or more of the methods set outherein.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice, e.g., the system described with reference to FIG. 4 . A computerreadable storage medium, as used herein, is not to be construed as beingtransitory signals per se, such as radio waves or other freelypropagating electromagnetic waves through a transmission media.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousaspects of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider) or in a cloud computing environment or offered as a servicesuch as a Software as a Service (SaaS).

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatuses(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general-purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable instruction executionapparatus, create a mechanism for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that when executed can direct a computer, otherprogrammable data processing apparatus, or other devices to function ina particular manner, such that the instructions when stored in thecomputer readable medium produce an article of manufacture includinginstructions which when executed, cause a computer to implement thefunction/act specified in the flowchart and/or block diagram block orblocks. The computer program instructions may also be loaded onto acomputer, other programmable instruction execution apparatus, or otherdevices to cause a series of operational steps to be performed on thecomputer, other programmable apparatuses or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of anymeans or step plus function elements in the claims below are intended toinclude any disclosed structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present disclosure has been presentedfor purposes of illustration and description but is not intended to beexhaustive or limited to the disclosure in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of thedisclosure. The aspects of the disclosure herein were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure with various modifications as aresuited to the particular use contemplated.

What is claimed is:
 1. A system comprising: a light source for emittingsubstantially monochromatic light; a linear polarizer having a firstpolarization axis, the linear polarizer for receiving the emitted lightand producing a linearly polarized light from the received light; a beamsplitter for receiving the linearly polarized light and redirecting thereceived light; an objective lens for receiving the redirected light andfocusing the redirected light onto a sample, the sample having anoptical anisotropy, the objective lens further for receiving a reflectedlight from the sample and propagating the reflected light to the beamsplitter, the reflected light being a function of the opticalanisotropy; an analyzer having a second polarization axis, the secondpolarization axis being generally orthogonal to the first polarizationaxis, the analyzer for receiving the propagated reflected light from thebeam splitter and producing a partially extinguished light, thepartially extinguished light being a function of the first polarizationaxis, the second polarization axis, and the optical anisotropy of thesample; a camera to capture an image sequence of the partiallyextinguished light; and a processing system for: flat-field correctingthe captured image sequence; performing image rotation and imageregistration; extracting an intensity response at each pixel of thecaptured image; extracting an amplitude value for the intensity responseat each pixel of the captured image sequence; extracting a phase valuefor the intensity response at each pixel of the captured image sequence;correlating the amplitude with an inclination of an optic axis;correlating the phase with an azimuth of the optic axis; and quantifyingmicrostructured regions (MTRs) using the inclination and azimuth.
 2. Thesystem of claim 1, wherein the light source comprises a light-emittingdiode (LED) having a wavelength of 660 nanometers (nm).
 3. The system ofclaim 1, wherein the light source comprises a helium-neon (HeNe) laserhaving a wavelength of 632 nanometers (nm).
 4. The system of claim 1,further comprising a quarter-wave plate located between the linearpolarizer and the beam splitter, the quarter-wave plate for receivingthe linearly polarized light and producing a circularly polarized lightfrom the linearly polarized light.
 5. The system of claim 1, wherein:the linear polarizer is fixed; the analyzer is fixed; and the sample isrotatable at designated rotation angles.
 6. The system of claim 1,wherein: the linear polarizer is rotatable at designated rotationangles; and the analyzer is rotatable to maintain cross-polarizationwith the linear polarizer.
 7. The system of claim 1, further comprisinga liquid crystal variable retarder located between the linear polarizerand the beam splitter that produces variable elliptical polarizationfrom the linearly polarized light.
 8. The system of claim 1, wherein thesample is laterally movable with reference to the camera.
 9. The systemof claim 1, wherein the light source, the polarizer, the beam splitter,the objective lens, the analyzer, and the camera are movable withreference to the sample.
 10. The system of claim 9, wherein the lightsource, the polarizer, the beam splitter, the objective lens, theanalyzer, and the camera are located on a gantry system.
 11. The systemof claim 1, wherein the objective lens focuses light onto a surface ofthe sample.
 12. The system of claim 1, wherein an image of the lightsource is focused on a back focal plane of the objective lens to producehomogeneous illumination on the sample.
 13. The system of claim 1,wherein the light source emits near-infrared (NIR) light.
 14. The systemof claim 1, further comprising a controller for controlling a rotationalangle of the sample.
 15. The system of claim 1, where the phase andamplitude are calculated using fast Fourier transform (FFT) analysis.16. A system comprising a light source for emitting light; an ellipticalpolarizer having a first polarization axis, the elliptical polarizer forreceiving the emitted light and producing a polarized light from thereceived light; a beam splitter for receiving the polarized light andredirecting the received polarized light; an objective lens forreceiving the redirected light and focusing the redirected light onto asample, the sample having an optical anisotropy, the objective lensfurther for receiving a reflected light from the sample and propagatingthe reflected light to the beam splitter, the reflected light being afunction of the optical anisotropy; an analyzer having an adjustablepolarization axis, the analyzer for receiving the propagated reflectedlight from the beam splitter and producing a partially extinguishedlight, the partially extinguished light being a function of theelliptical polarization, the alignment of the adjustable polarizationaxis, and the optical anisotropy of the sample; a camera to capture animage sequence of the partially extinguished light; and a processingsystem for: flat-field correcting the captured image sequence;performing image rotation and image registration; extracting anintensity response at each pixel of the captured image sequence;correlating the inclination with the intensity response using a forwardmapping; correlating the azimuth with the intensity response using aforward mapping; and quantifying microstructured regions (MTRs) usingthe inclination and azimuth.
 17. The system of claim 16 wherein theanalyzer is a rotatable and linearly polarized analyzer.
 18. The systemof claim 16 wherein the analyzer is a circular polarizing analyzer. 19.The system of claim 16 wherein the elliptical polarizer comprises twoliquid crystal variable retarders wherein: the polarized light receivedby the beam splitter is the elliptically polarized light; and theanalyzer is a circularly polarized analyzer.